Experimental Verification of the Knowledge-Aided Approach for Active Target Localization in Reverberant Environments

A knowledge-aided approach for active target localization is presented and applied to the real-sea data to verify its feasibility in snapshot-deficient scenarios in the presence of reverberation. By leveraging the a priori reverberation covariance computed from a reverberation model, the knowledge-aided minimum variance distortionless response (KA-MVDR) is derived to facilitate adaptive processing and thus localize weak targets in reverberation. The KA-MVDR-based bearing-time record is employed to analyze the experimental data collected in the South China Sea, which verifies that the weak target echoes can be localized using single ping data with significantly reduced reverberation levels. Moreover, the impact of the bottom roughness mismatch on the model-based reverberation covariance and the performance of KA-MVDR are evaluated by the output signal-interference-plus-noise ratio, manifesting the robustness of this approach in real-sea applications.